Application of self-organizing maps for the detection and classification of human blood plasma lipoprotein lipid profiles on the basis of 1H NMR spectroscopy data.

Efficient and relevant classification of clinical findings, i.e. diagnostic decision making, poses a major challenge in medicine. In relation to biomedical NMR spectroscopy the problem of classification is often accompanied by complex, heavily overlapping information. Self-organizing map (SOM) analysis has been successfully applied in many areas of research and was thus also considered as a potential tool for NMR data analysis. In this paper we demonstrate how SOM analysis can be used for automated NMR data classification. Our goal was analysis of plasma lipoprotein lipids, a complex but biochemically well understood and specified system. The results illustrate that clinically relevant lipid classifications can be obtained from the SOM analysis of 1H NMR spectral information alone. The resulting maps were calibrated using independent biochemical lipid analyses and were found to produce excellent clustering of the plasma samples into clinically useful groups: normal, type IIa, IIb and IV hyperlipidaemias. In addition to this traditional classification, we also present results from SOM analysis in which the reference vectors of the map were calibrated for plasma total cholesterol and triglycerides and high and low density lipoprotein C; the plasma lipid parameters that are currently considered as the most useful indicators of coronary heart disease risk. In all, the present results indicate that SOM analysis can cope well with complex NMR spectral information and is thus likely to have an independent role in the area of biomedical NMR data analysis.